axmat / parallelkmeans

Parallel K-Means Clustering Algorithm

Parallel K-Means clustering algorithm

K-Means algorithm

1. Initialize the centers using kmeans++ algorithm
2. Assign each point to the cluster with the nearest
3. Update the clusters centers
4. Repeats step 2 and 3 until convergence

Data set

The data set was generated from a gaussian mixture distribution.

Clusters obtained by runing the sequential Kmeans clustering algorithm.

Dependencies

• C++ compiler with c++-17 support (tested on clang)
• Make
• [Optional] Julia (DataFrames, CSV and Plots packages)

Building

To build run make

Results

The following results are obtained for:

• number of clusters : 3
• number of threads: 32
• number of repetitions: 30
• maximum number of iterations: 1000
• threshold : 0.1

To reproduce the results run ./benchmark 3 32 30 1000 0.1

Benchmark

Speedup

Conclusion

• Used OPenMP for parallelization
• Used speedup to evaluate the performances of the parallelized kmeans algorithm
• Maximum speedup is ~7x

References

1. T Kanungo, DM Mount, NS Netanyahu, C.D. Piatko, R. Silverman, A.Y. Wu. An efficient k-means clustering algorithm: Analysis and implementation, in IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 24, no. 7, pp. 881-892, July 2002.
2. Arthur, David and Sergei Vassilvitski. k-means++: The Advantages of Careful Seeding, 2007.
3. Barbara Chapman, Gabrielle Jost, Ruud Van Der Pas. Using OpenMP, 2007.
4. Joe Pitt-Francis, Jonathan Whiteley. Guide to Scientific Computing in C++, Second Edition, 2017.
5. OpenMP 4.5 Reference Guide – C/C++